On the Implementation of a Digital Watermarking Based on Phase Congruency

Basu, Amitabha; Saha, Arindam; Das, Jeet; Roy, Sandipta; Mitra, Sushavan; Mal, Indranil; Sarkar, Subir Kumar

doi:10.1007/978-3-319-12012-6_13

Cited by 3 publications

(1 citation statement)

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“…Currently, the space domain-and frequency domainbased are the main methods of the cover image pre-treatment for information hiding [12]. LSB is the basic algorithm of space domain methods, but the schemes based on LSB almost have drawbacks under embedding great amount of information [1]. Methods in frequency domain, such as DCT and DWT, have robustness against certain attacks, but are costly and of low robustness against rotating attacks [10].…”

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confidence: 99%

Characteristic analysis of carrier based on the filtering and a multi-wavelet method for the information hiding

Ren¹,

Zhang²

2015

Discrete &Amp; Continuous Dynamical Systems - S

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The characteristic consistency between the original and the stego carriers is an important indicator to evaluate an information hiding algorithm. Different from the traditional carrier pre-processing methods which are based on the operation domains, we propose a characteristics analysis-based preprocessing scheme. We use the Gaussian pyramid filtering and CARDBAL2 multi-wavelet transform to analyze the energy characteristics of the carrier, so the original carrier can be decomposed into several sub-regions with different energy level. And at the same time, the processed carrier shows us the redundancy space structurally through the combination bit plane method, which can provide some invisible hiding positions. Obviously, the energy and structure characteristics are at least related with the robustness and invisibility of the hiding result respectively. So we can improve these performances compared with the traditional methods. At the same time, some optimization theories like the Chebyshev map are used to improve other performances. At last, the experimental shows the achievements of this scheme in the form of data.

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confidence: 99%